Sixth Version Research Articles

What Changed in CNS5? A Mini-Review on General Changes and Adult Diffuse Gliomas.

The fifth edition of the WHO classification of tumors of the central nervous system (WHO CNS5) was published in 2021 which is the sixth version of the international standard for the diagnostics of CNS tumors. Regular updates of the consortium to inform molecular and practical approaches to CNS tumor taxonomy (cIMPACT-NOW) shaped the WHO CNS5 which continues the trend of incorporating the molecular characteristics of tumors into the histological and immunohistochemical findings. The various updates can be classified into general changes across all tumors and specific changes within the tumor groups. This mini-review highlights the general changes and the major changes in adult diffuse gliomas.

Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset

Abstract. Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3×106 wildfires in the US between 1992 and 2020 . For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at https://doi.org/10.5281/zenodo.8381129 (Pourmohamad et al., 2023).

Projections of temperature and precipitation trends using CMhyd under CMIP6 scenarios: A case study of Iraq's Middle and West

Climate change is becoming a leading issue in the 21st century due to its devastating environmental and socioeconomic impacts. It is believed to have significant impacts on the water resources and groundwater in the Middle East region, especially in Iraq. In this study, three different global climate models, under the 6th IPCC report, were downscaled under two SSPs scenarios in the middle and west of Iraq. The downscaled models were fixed for bias using CMhyd (climate model data for hydrologic modeling) over the study area. This was achieved using data from four locally chosen weather stations from 1983 to 2022. Three CMIP6 global models were considered and compared. These were the Australian Community Climate and Earth-System Simulator version 1 (ACCESS-CM2), the Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR), and the sixth version of the Model for Interdisciplinary Research on Climate (MIROC6).The findings of this study show that the temperature and precipitation changes in the three models under two scenarios in the future period (2023–2050) show an increasing trend compared to the observation period (1983–2014). The results of the generated predictions show that there will be an increase in annual maximum temperatures by this century's end, by 1.26–2.08 °C under scenario SSP2–4.5 and 1.57–2.34 °C under scenario SSP5–8.5 across all study stations. The study found that the annual increase in precipitation over the observed period varies between scenarios (SSP2–4.5 and SSP5–8.5), ranging from +3.56 to +3.87% for SSP2–4.5 and + 7.22% to +12.61% for SSP5–8.5. The results should help improve our understanding of the effects of climate change on the study region and encourage planners and stakeholders to identify optimal strategies for mitigating them.

Assessing groundwater drought vulnerability through baseflow separation and index-based analysis under climate change projections

Groundwater is an essential resource, providing drinking water to millions of people globally and supporting agriculture, industry, and ecosystems. It acts as a drought buffer and provides a stable source of water during dry periods. However, groundwater is often underestimated and overexploited, leading to aquifer depletion and other negative impacts. Groundwater is invisible and difficult to measure, which makes it challenging for sustainable management. This study employed the Soil & Water Assessment Tool (SWAT) hydrological model to simulate streamflow in an eastern Afghan watershed using satellite-based and reanalysis data. Variables from five Coupled model intercomparison project phase 6 (CMIP6) general circulation models (GCMs), including minimum and maximum temperature and precipitation, were bias-corrected (downscaled) using Quantile Mapping (QM). The results from these five GCMs were then compared based on statistical criteria to determine the most reliable model, and the selected model was used to derive future data (i.e., 2021 to 2050) under different climate scenarios after applying QM. Using the future climate data and the developed SWAT model, streamflow values were projected for future periods. Considering that there are various methods to estimate groundwater recharge and the most commonly used technique involves baseflow separation procedures, this study used seven baseflow separation methods to estimate groundwater recharge values based on streamflow time series from the baseline period and different climate scenarios. To evaluate the groundwater drought characteristics, a recently proposed index called the Groundwater Recharge Drought Index (GRDI) and the run theory method were used. Results indicated that the SWAT model performed well in simulating and predicting streamflow. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC6) provided the highest accuracy compared to other climate models. Moreover, it was observed that the baseflow separation methods significantly affected the estimation of groundwater recharge, thus the obtained results from all developed methods were considered. The log-normal function was found to provide the best correlation for estimating GRDI. The findings revealed a gradual decrease in drought duration in the coming years. However, the magnitude and severity of droughts are projected to increase, especially under pessimistic climate scenarios.

Modeling future (2021–2050) meteorological drought characteristics using CMIP6 climate scenarios in the Western Cape Province, South Africa

Consistent drought modelling under plausible shared socioeconomic–representative concentration pathways (SSP–RCPs) are crucial for effectively managing future drought risk in agricultural environments. The Western Cape (WC) is one of South Africa’s main agro-based provinces and faces a mounting threat of water insecurity due to recurrent drought. The objective of this study was to predict meteorological drought hazard for 2021–2050 based on three CMIP6 scenarios: SSP5–8.5, SSP2–4.5 and SSP1–2.6. Precipitation simulations generated by the sixth version of Model for Interdisciplinary Research on Climate (MIROC6) under the SSP5–8.5, SSP2–4.5 and SSP1–2.6 scenarios were used from fifteen stations across the six AEZs of the WC province. The Standardised Precipitation Index (SPI) was computed at 12-month timescales. Trend analysis of precipitation datasets and the SPI-values were done at p < 0.05 using the Mann–Kendall (M–K) test. The findings revealed negative precipitation trends of − 7.6 mm/year in Ceres, while positive trends of 0.3 mm/year were observed in Malmesbury. These findings indicate an improvement from − 7.8 and − 6.4 mm/year in the same regions, respectively, compared to historical trends observed between 1980 and 2020. The results suggest that in 2042 and 2044, Bredasdorp will experience − 2 < SPI < − 1.5 under the SSP2–4.5 scenarios, while Matroosberg in 2038 under the SSP5–8.5 will experience SPI > − 2. The findings of this study will assist in the development of proactive planning and implementation of drought mitigation strategies and policies aimed at reducing water insecurity in AEZs.

The Saudi initiative for asthma - 2024 update: Guidelines for the diagnosis and management of asthma in adults and children.

The Saudi Initiative for Asthma 2024 (SINA-2024) is the sixth version of asthma guidelines for the diagnosis and management of asthma for adults and children that was developed by the SINA group, a subsidiary of the Saudi Thoracic Society. The main objective of the SINA is to have guidelines that are up-to-date, simple to understand, and easy to use by healthcare workers dealing with asthma patients. To facilitate achieving the goals of asthma management, the SINA Panel approach is mainly based on the assessment of symptom control and risk for both adults and children. The approach to asthma management is aligned for age groups: adults, adolescents, children aged 5-12 years, and children aged <5 years. SINA guidelines have focused more on personalized approaches reflecting a better understanding of disease heterogeneity with the integration of recommendations related to biologic agents, evidence-based updates on treatment, and the role of immunotherapy in management. The medication appendix has also been updated with the addition of recent evidence, new indications for existing medication, and new medications. The guidelines are constructed based on the available evidence, local literature, and the current situation at national and regional levels. There is also an emphasis on patient-doctor partnership in the management that also includes a self-management plan.

Milwaukee Protocol — from idea to implementation

Despite the fact that rabies in the modern world is a rare sporadic disease, the relevance of its methods of treatment is beyond doubt, since in the absence of timely prevention, it is characterized by 100% mortality. As of 2020, there are about 70 cases of experimental rabies treatment protocols according to the Milwaukee Protocol Rabies Registry. The very idea that the human body can cope with the virus itself has been around for a long time. Even before the advent of the Milwaukee protocol, there were attempts at treatment at the stage of clinical manifestations. The first was Michael Huttwick of the Center for Infectious Disease Control in Atlanta, although the scientific community is still arguing whether it was really rabies. The essence of this protocol is the introduction of a patient into an artificial coma, the introduction of antiviral drugs, symptomatic therapy (anticolvusants, mechanical ventilation, etc.). At the moment, there is the sixth version of the Milwaukee Protocol (from 2018), which provides general recommendations for its maintenance, since the material base is too small to draw any general conclusions.

Analysis of the Development Mechanisms of a Polar Low over the Norwegian Sea Simulated with the Canadian Regional Climate Model

Polar lows (PLs) are maritime mesoscale cyclones associated with severe weather. They develop during marine cold air outbreaks near coastlines and the sea ice edge. Unfortunately, our knowledge about the mechanisms leading to PL development is still incomplete. This study aims to provide a detailed analysis of the development mechanisms of a PL that formed over the Norwegian Sea on 25 March 2019 using the output of a simulation with the sixth version of the Canadian Regional Climate Model (CRCM6/GEM4), a convection-permitting model. First, the life cycle of the PL is described and the vertical wind shear environment is analysed. Then, the horizontal wind divergence and the baroclinic conversion term are computed, and a surface pressure tendency equation is developed. In addition, the roles of atmospheric static stability, latent heat release, and surface heat and moisture fluxes are explored. The results show that the PL developed in a forward-shear environment and that moist baroclinic instability played a major role in its genesis and intensification. Baroclinic instability was initially only present at low levels of the atmosphere, but later extended upward until it reached the mid-troposphere. Whereas the latent heat of condensation and the surface heat fluxes also contributed to the development of the PL, convective available potential energy and barotropic conversion do not seem to have played a major role in its intensification. In conclusion, this study shows that a convection-permitting model simulation is a powerful tool to study the details of the structure of PLs, as well as their development mechanisms.

A novel hybrid transformer-CNN architecture for environmental microorganism classification.

The success of vision transformers (ViTs) has given rise to their application in classification tasks of small environmental microorganism (EM) datasets. However, due to the lack of multi-scale feature maps and local feature extraction capabilities, the pure transformer architecture cannot achieve good results on small EM datasets. In this work, a novel hybrid model is proposed by combining the transformer with a convolution neural network (CNN). Compared to traditional ViTs and CNNs, the proposed model achieves state-of-the-art performance when trained on small EM datasets. This is accomplished in two ways. 1) Instead of the original fixed-size feature maps of the transformer-based designs, a hierarchical structure is adopted to obtain multi-scale feature maps. 2) Two new blocks are introduced to the transformer's two core sections, namely the convolutional parameter sharing multi-head attention block and the local feed-forward network block. The ways allow the model to extract more local features compared to traditional transformers. In particular, for classification on the sixth version of the EM dataset (EMDS-6), the proposed model outperforms the baseline Xception by 6.7 percentage points, while being 60 times smaller in parameter size. In addition, the proposed model also generalizes well on the WHOI dataset (accuracy of 99%) and constitutes a fresh approach to the use of transformers for visual classification tasks based on small EM datasets.

Manufacturing planning and control driven supply chain risk management: A dynamic capability perspective

This paper builds on the dynamic capability view to theorize the role of manufacturing planning and control (MPC) activities and supply chain risk management (SCRM) capabilities towards the firm's operational performance. The study hypothesize that companies enhance MPC activities to respond to supply chain uncertainty (SCU) and enable SCRM that positively impacts operational performance. Data from 356 manufacturing companies in developing countries and regions, drawn from the sixth version of the International Manufacturing Strategy Survey is used to examine the hypothesized model empirically. The findings indicate that the MPC activities effectively respond to SCU and act as an enabler of preventive and reactive SCRM. Furthermore, the paper finds that MPC activities drive operational performance through effective SCRM. Also, the findings suggest that preventive risk management practices impact operational performance only through reactive risk management. Finally, the paper enriches the literature by identifying and discussing the theoretical and managerial significance of the role of MPC activities in the association between SCU, risk management practices, and firm performance.

Comparative Study for Patch-Level and Pixel-Level Segmentation of Deep Learning Methods on Transparent Images of Environmental Microorganisms: From Convolutional Neural Networks to Visual Transformers

Currently, the field of transparent image analysis has gradually become a hot topic. However, traditional analysis methods are accompanied by large amounts of carbon emissions, and consumes significant manpower and material resources. The continuous development of computer vision enables the use of computers to analyze images. However, the low contrast between the foreground and background of transparent images makes their segmentation difficult for computers. To address this problem, we first analyzed them with pixel patches, and then classified the patches as foreground and background. Finally, the segmentation of the transparent images was completed through the reconstruction of pixel patches. To understand the performance of different deep learning networks in transparent image segmentation, we conducted a series of comparative experiments using patch-level and pixel-level methods. In two sets of experiments, we compared the segmentation performance of four convolutional neural network (CNN) models and a visual transformer (ViT) model on the transparent environmental microorganism dataset fifth version. The results demonstrated that U-Net++ had the highest accuracy rate of 95.32% in the pixel-level segmentation experiment followed by ViT with an accuracy rate of 95.31%. However, ResNet50 had the highest accuracy rate of 90.00% and ViT had the lowest accuracy of 89.25% in the patch-level segmentation experiments. Hence, we concluded that ViT performed the lowest in patch-level segmentation experiments, but outperformed most CNNs in pixel-level segmentation. Further, we combined patch-level and pixel-level segmentation results to reduce the loss of segmentation details in the EM images. This conclusion was also verified by the environmental microorganism dataset sixth version dataset (EMDS-6).

Systematic Review on Eating Disorder Examination-Questionnaire 6.0 (EDE-Q 6.0): An Agenda for a Test Adaptation in Malaysia

This study aims to systematically search and review the literature on the Eating Disorder Examination-Questionnaire 6.0 (EDE-Q 6.0) across cultures, in terms of their psychometric properties. Electronic search engines and databases, namely, Scopus, OvidMedline, Google Scholar, and reference tracking were used. The search terms used were eating disorder examination-questionnaire, EDE-Q, psychometrics, reliability, validity, and utility. Studies were included if they conducted psychometric evaluations on the EDE-Q 6.0 (i.e., English version) or adapted EDE-Q 6.0. The PRISMA guideline was also applied in the selection of articles for review. Nineteen papers on EDE-Q, specifically on the sixth version were reviewed. EDE-Q 6.0 has been translated and adapted to various languages and psychometrically evaluated on Japanese, Portuguese, American, Finnish, French, Norwegian, British, Australian, Italian, Iranian, and German populations. EDE-Q 6.0 has recorded good to excellent internal reliability across languages and cultures. Mixtures of findings were found concerning its stability and validity. The factorial structure of EDE-Q 6.0 is very controversial with limited fitness into its initial theoretical four-factor structure. Nonetheless, EDE-Q 6.0 recorded high utility values. The EDE-Q 6.0 is yet to be adapted into Malay for the Malaysian population, hence, calls for further research on its adaptation and its psychometric properties. Keywords: Systematic review; EDE-Q 6.0; test adaptation; test validation

Update of the Xylella spp. host plant database - systematic literature search up to 31 December 2021.

This Scientific report provides an update of the Xylella spp. host plant database, aiming to provide information and scientific support to risk assessors, risk managers and researchers dealing with Xylella spp. Upon a mandate of the European Commission, EFSA created and regularly updated a database of host plant species of Xylella spp. The current mandate covers the period 2021–2026. This report is related to the sixth version of the database published in Zenodo in the EFSA Knowledge Junction community, covering literature published from 1 July 2021 up to 31 December 2021, and recent Europhyt outbreak notifications. Informative data have been extracted from 29 selected publications. Eleven new host plants were identified and added to the database: six plant species naturally infected by subsp. multiplex of X. fastidiosa in the EU (France, Italy and Portugal) and five plant species artificially infected by different X. fastidiosa subspecies (multiplex, pauca, fastidiosa and sandyi). No additional data were retrieved for X. taiwanensis. New information on the tolerant/resistant response of plant species to X. fastidiosa infection were added, while no new STs have been identified worldwide compared to the previous update published in January 2022. The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 412 plant species, 190 genera and 68 families. Such numbers rise to 664 plant species, 299 genera and 88 families if considered regardless of the detection methods applied.

Suspended particulate matter and secchi disk depth in the Chilika Lagoon from in situ and remote sensing data: a modified semi-analytical approach

ABSTRACT Increasing sediment load and deteriorating water clarity are the key challenges for many inland water bodies. The uncertainty associated with the bio-optical complexity of these water bodies limits remote-sensing approaches to monitor such fragile ecosystems. Therefore, we measured optical properties along with water quality parameters in the Chilika lagoon, the second-largest brackish water lagoon in the world. We evaluated exiting quasi-analytical algorithms (QAAs) to retrieve particle back-scattering coefficients (b bp) and diffuse attenuation coefficient (K d) from surface remote-sensing reflectance (R rs) data. However, existing QAAs underestimated the observed b bp and K d values in the Chilika lagoon. Therefore, a modified inversion algorithm (MIA) using the combined features of the sixth version of QAA (QAA v6) and the IOPs inversion model of inland waters (IIMIW) is proposed and validated. Values of b bp at 665 nm and K d at 490 nm were then used to estimate suspended particulate matter (SPM) and Secchi disk depth (ZSD), respectively. The bias for b bp and K d was significantly reduced using MIA, which improved the accuracy of SPM and ZSD estimation. We further used MIA to retrieve b bp(665) and K d(490) values from the Sentinel 2A/2B (S2A/S2B) multispectral instrument (MSI) R rs data for each pixel over the Chilika lagoon to estimate SPM and ZSD respectively. Better root-mean-squared error (RMSE) value of 10.91 mg L−1 for SPM (ranging from 11.54 to 99.00 mg L−1) and 0.21 m for ZSD (ranging from 0.15 to 1.2 m) was observed when compared with in situ measurements (n = 77). The mean SPM trend from 90 different S2A/S2B MSI acquisition dates from 2017 to 2021 indicated that SPM concentration in the lagoon is closely related to wind velocity. Matching fish landing trends with SPM concentrations was also observed, which may open new opportunities to manage inland water bodies using remote-sensing observations.

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation.

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

Lean, sustainability and the triple bottom line performance: a systems perspective-based empirical examination

PurposeThis study draws on the systems perspective to study the individual and combined interaction effect of lean management (LM) and sustainability management (SM) on the organization's triple bottom line (TBL) performance.Design/methodology/approachThe study employs structural equation modeling to test the proposed hypotheses using data from the sixth version of the International Manufacturing Strategy Survey (IMSS VI).FindingsThe study finds that LM is positively related to all dimensions of the TBL performance. In contrast, SM is positively related to social and environmental performance and negatively related to economic performance. Finally, by finding that the interaction between LM and (SM) is positive for social and environmental performance, this study not only confirms that LM is an enabler for sustainability, but it also supports that the two paradigms are mutually compatible and reinforcing.Practical implicationsThe findings imply that practitioners pursuing both LM and SM should leverage their mutual positive effects and balance the unintended effects of implementing isolated bundles by implementing them together as a complete socio-technical system. Their combined impact on the TBL performance will outweigh the sum of their individual effects in the case of isolated implementations.Originality/valueIn contrast with the extant literature, this study proposes that LM and SM make parts of one system as opposed to one correlated with the other or having a positive causal effect on the other. Taking an integrated systems approach, the study empirically verifies the “mutual compatibility” of the lean and sustainability paradigms argument, with regard to their effect on the TBL performance.

Projected temperature and precipitation changes using the LARS‐WG statistical downscaling model in the Shire River Basin, Malawi

AbstractThis study analyzes local‐scale temperature and precipitation projections in the Shire River Basin (SRB) in Malawi using 10 global circulation models (GCMs) available in the coupled model intercomparison project phase 5 (CMIP5), under two representative concentration pathways (RCP 4.5 and RCP 8.5). For nine stations in the study area, large‐scale maximum temperature (Tmax), minimum temperature (Tmin) and precipitation data from the selected GCMs were downscaled by the sixth version of the Long Ashton Research Station Weather Generator (LARS‐WG6). The mean seasonal and annual change projections for Tmax, Tmin and precipitation during two future periods, that is, the middle future (2041–2070) and late future (2071–2100) periods were analyzed. Modelling results demonstrated that the LARS‐WG model is capable of simulating temperature more accurately than precipitation in the SRB. All 10 GCMs revealed that continually rising temperatures are anticipated in the study area; however, the projected magnitude of change varied across GCMs and between RCPs. Generally, the increase in average Tmax and Tmin was observed to be higher under RCP 8.5 compared with RCP 4.5 due to unmitigated greenhouse gas emissions (GHGs). Future precipitation change results showed more complexity and uncertainty than for temperature; not all GCMs agree on whether there will be positive or negative changes in precipitation and no systematic variations under RCP4.5 and RCP8.5 were observed during the two future time period, illustrating that both GCMs and RCPs are important sources of the relatively large uncertainties in future precipitation projections in the SRB. Thus, this study indicated that uncertainties constrained by both GCMs and RCPs are crucial and need to always be considered when executing climate impact studies and adaptation, particularly at river basin level.

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary.

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

The Role of distance learning via the Internet in Learning Programming: Learners' Point of View

The current research aims toidentify the role of distance learning via the Internet in learning programming for fourth-preparatory school students, as it is their first experience in the field of learning and application in the 2018-2019 academic year. one of the main reasons for resorting to this type of learning is the limited time of the weekly lessonof learning programming, within the computer lessons scheduled as one session per week for the fifth preparatory school students. This leads to deficiencies in some scientific aspects of programming, which are considered one of the most important demands of development and the needs of the present era that the learners must be adequately trained on. This experiment was applied to the community of 100 learners, by providing computerlessons for programming in the language of Visual Basic, in its sixth version, and sending those lessons through the educational group assigned to them through the simplest social media sites so that answers clarify the role of this learning from the sample consisting of 40 participants. Their opinions were obtained by applying the questionnaire. The research found that distance learning via the Internet has a significant role in achieving the goals that were set by the Ministry of Education for the computer subject, specifically the programming forthe fourth preparatory class, which included the skill, cognitive and emotional field. It was found that there is a high degree of accomplishment achieved by distance learning via the Internet was for the emotional domain. The study suggested a set of statements related to the interest in this type of learning and the pursuit of its development to be used in an ideal manner to help the learners.

MIPLIB 2017: data-driven compilation of the 6th\xa0mixed-integer programming library

We report on the selection process leading to the sixth version of the Mixed Integer Programming Library, MIPLIB 2017. Selected from an initial pool of 5721 instances, the new MIPLIB 2017 collection consists of 1065 instances. A subset of 240 instances was specially selected for benchmarking solver performance. For the first time, these sets were compiled using a data-driven selection process supported by the solution of a sequence of mixed integer optimization problems, which encode requirements on diversity and balancedness with respect to instance features and performance data.